8 October 1996 Imposed measure approach to stochastic clutter characterization
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Stochastic clutter can often be modeled as a piecewise stationary random field. The individual stationary subregions of homogeneity in the field can then be characterized by marginal density functions. This level of characterization is often sufficient for determination of clutter type on a local basis. We present a technique for the simultaneous characterization of the sub-regions of a random field based on semiparametric density estimation on the entire random field. This technique is based on a borrowed strength methodology that allows the use of observations from potentially dissimilar subregions to improve local density estimation and hence random process characterization. This approach is illustrated through an application to a set of digitized mammogram images which requires the processing five million observations. The results indicate that there is sufficient similarity between images, in addition to the more intuitively obvious within- image similarities, to justify such a procedure. The results are analyzed for the utility of such a procedure to produce superior models in terms of 'stochastic clutter characterization' for target detection applications in which there are variable background processes.
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George W. Rogers, George W. Rogers, Tim E. Olson, Tim E. Olson, Carey E. Priebe, Carey E. Priebe, David J. Marchette, David J. Marchette, } "Imposed measure approach to stochastic clutter characterization", Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); doi: 10.1117/12.253455; https://doi.org/10.1117/12.253455

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